STSA - The Statistical Time Series Analysis Toolbox for O-MatrixVersion Enhancements and Additions Summary

The STSA (Statistical Time Series Analysis) Toolbox Version 2.1 release
includes a large number of enhancements and additional functions.
Some of the new areas of functionality include:

There are 2 new sub-directories, or functional categories
that expand the existing capabilities of STSA:
POD; Proper Orthogonal Decomposition and Singular Spectrum Analysis, and
NONPAR; functions for nonparametric, nonlinear time series analysis.

All sub-directories have been updated, enhanced, and expanded with
new functions - STSA can now handle a greater array of time series problems.

More functions have been added that can be used and in
non-time series contexts: more random number generators, cumulative distribution
functions, probability density functions, statistical tools, and
generic optimization.

Some specific version 2.1 enhancements include:

Functions for performing singular spectrum analysis (SSA) of a
time series including decomposition,
reconstruction and forecasting.

Functions for handling nonlinear time series using
nonparametric models,
including local polynomials, cubic splines, functional
coefficient models, partially linear models and various cross
validation methods for automated bandwidth selection.

More examples that illustrate and expand
on existing and new functional capabilities.

New samples using provide real-world, and simulated data sets.

The STSA Toolbox Version 2.0 release
included a large number of enhancements and additional functionality
including:

Four new sub-directories (FILTER, OPTIMIZE, RNG & STATS) that expand
the capabilities of STSA.

All sub-directories have been updated and expanded with new functions -
STSA can now handle a greater array of time series problems.

STSA now contains additional functions that can be used and in non-time series
contexts (random numbers, statistical tools, generic optimization).

Many functions now contain formatted screen output that greatly enhances the
speed and quality of any analysis.

Some specific version 2.0 enhancements included:

Compute the theoretical autocovariances of an ARMA model.

Durbin-Levinson-Whittle algorithm for computing innovations.

Perform Granger causality tests.

Compare forecasting performance of competing models.

Compute robust estimates using Least Absolute Deviations.

Filter a time series using a variety of filtering methods and models
(Savitzky- Golay, generic finite impulse response, time-invariant Kalman filter
with estimation, Holt-Winters with seasonal and estimation).